There is an increasing need to analyze multivariate time series data due to the rapid development of data collection tools such as smartphone APPs, wearable sensors, and brain imaging techniques. P-technique factor analysis allows researchers to establish a measurement model for these time series. Analyzing such data is challenging because they are often non-normal (e.g., steps, heart rate, sleep, mood, and brain signals) and correlated at nearby time points. We propose using a bootstrap procedure to accommodate both the non-normality and the dependency of nearby time points. We explore the statistical properties with simulated data and illustrate the test with two empirical data sets. The results of the simulation study include (1) the bootstrap procedure performed better than an existing analytic procedure for time series data with excessive kurtosis (2) an existing analytic procedure performed better than the bootstrap procedure for normal time series and skewed time series.